Introduction

Advances in medical imaging technology have made it possible to routinely acquire high-resolution, three-dimensional images of human anatomy and function using a variety of imaging modalities. Up to several thousands of images can be acquired for the diagnosis of complex cases. This large amount of images per case, together with the growing importance of medical imaging in clinical practice, have continuously increased the workload of the radiologist, which explains the need for computer-assisted medical image computing. Furthermore there is a quest for objective, quantitative information from medical images. In radiotherapy, for instance, outlines of the irradiation volume and the neighboring organs at risk are delineated in 3D images and used to calculate a suitable radiation therapy. In neurology, degenerative diseases of the brain, such as multiple sclerosis, Alzheimer's, or schizophrenia, are studied by measuring brain shape and tissue changes in MR images. In cardiology, the health condition of the heart is assessed by studying the dynamics, the perfusion, and tissue characteristics of the heart muscle as revealed by MR or ultrasound images, and so forth. Today medical image computing plays a role in early patient diagnosis, individualized therapy planning, population screening, therapy outcome prediction and assessment, and translational pre-clinical and clinical research.

Traditionally, medical images are interpreted by visual inspection of the images displayed slice by slice. Such radiological protocol is necessarily subjective, as it is based on the perception by a human observer and is usually restricted to mere qualitative statements and judgments. Moreover, the traditional 2D display of 3D images allows immediate inspection of anatomical structures in the two dimensions of the image plane only, whereas the third dimension has to be reconstructed mentally by the radiologist by looking at adjacent image slices. The use of 3D image visualization techniques makes direct inspection of the scene in three dimensions feasible and facilitates the extraction of quantitative information from the images. However, 3D visualization (see Chapter 8) typically needs filtered and sparse data, which implies intelligent preprocessing of the images.